Biometric identification system using pulse waveform

ABSTRACT

A biometric identity confirmation system is based on pulse waveform data for the subject. During an initial enrollment mode, pulse waveform data for a known subject are used to generate subject characterization data for the known subject. The subject characterization data includes an exemplar created by synchronous averaging of pulse waveform data over multiple pulse cycles. A number of trigger candidate are identified for the start point of each pulse cycle. The time delay between trigger candidates is analyzed to discard false trigger candidate and identify true candidates, which are then used as the start points for each pulse cycle in synchronous averaging of the pulse waveform data. During a subsequent identity authentication mode, pulse waveform data for a test subject are analyzed using the subject characterization data to confirm whether the identity of the test subject matches the known subject.

RELATED APPLICATIONS

The present application is a continuation-in-part of the Applicants'co-pending U.S. patent application Ser. No. 13/079,219, “entitled“Biometric Identification System Using Pulse Waveform,” filed on Apr. 4,2011. The present application is also a continuation-in-part of theApplicants' co-pending U.S. patent application Ser. No. 13/739,224,entitled “System for Biometric Identity Confirmation,” filed on Jan. 11,2013, which claims priority to U.S. Provisional patent application61/589,084, filed on Jan. 20, 2012.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to the field of biometricidentity confirmation. More specifically, the present inventiondiscloses a system for biometric identity confirmation based on analysisof pulse waveform data for a test subject.

2. Background of the Invention

Biometric identification is the process of recognizing or rejecting anunknown person as a particular member of a previously characterized set,based on biological measurements. The ideal biometric characterizationis specific to the individual, difficult to counterfeit, robust tometabolic fluctuations, insensitive to external conditions, easilymeasured, and quickly processed.

Fingerprint, retinal, iris, and facial scans are well-known biometricidentification techniques relying on image processing. Images aretwo-dimensional, requiring sophisticated and computationally intensivealgorithms, the analysis of which is often complicated by randomorientation and variable scaling. Voice recognition is an example ofbiometric identification amenable to time series analysis, an inherentlysimpler one-dimensional process.

The simplest biometric identifiers can be expressed as a singleparameter, such as height or weight. Single parameter identifiers havebeen the only quantitative means of identification throughout most ofhistory. The price of simplicity is the loss of specificity, and in thecase of weight, the lack of constancy over time. Nevertheless,single-parameter biometrics remain effective identifying factors, as isobvious from their continued use.

Identity tracking/confirmation is the process of following thewhereabouts of a known subject moving unpredictably among similarindividuals, perhaps with deceptive intent. Tracking/confirmation issomewhat simpler than identification, because it merely requiresdistinguishing the subject from all others rather than distinguishingevery individual from every other, and because continuous rather thanepisodic data are available. Biometric identity tracking/confirmation isthe continuous verification that a body-mounted sensor has remained onthe subject, and has not been surreptitiously transferred to animpostor. For the purposes of this application, the term “biometricidentification” should be broadly construed to encompass both biometricidentification in its narrower sense, as described above, and identitytracking/confirmation.

SUMMARY OF THE INVENTION

This invention provides a system for biometric identity confirmationbased on pulse waveform data for the test subject. During an initialenrollment mode, pulse waveform data for a known subject are used togenerate subject characterization data for the known subject. Thesubject characterization data includes an exemplar created bysynchronous averaging of pulse waveform data over multiple pulse cycles.A number of trigger candidate are identified for the start point of eachpulse cycle. The time delay between trigger candidates is analyzed todiscard false trigger candidate and identify true candidates, which arethen used as the start points for each pulse cycle in synchronousaveraging of the pulse waveform data. During a subsequent identityauthentication mode, pulse waveform data for a test subject are analyzedusing the subject characterization data to confirm whether the identityof the test subject matches the known subject.

These and other advantages, features, and objects of the presentinvention will be more readily understood in view of the followingdetailed description and the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention can be more readily understood in conjunction withthe accompanying drawings, in which:

FIG. 1 is a flowchart of the enrollment mode of the present invention.

FIG. 2 is a flowchart of the identity authentication mode of the presentinvention.

FIG. 3 is a flowchart of the “acquire trial” procedure.

FIG. 4 is a flowchart of the procedure used to enroll a new client.

FIG. 5 is a flowchart of the identity authentication mode of the presentinvention.

FIG. 6 is a graph showing pulse waveform exemplar shape vectors of theten subjects of a recent study, along with the mean pulse waveform.

FIG. 7 is a graph showing an example of an enrollment trial with raileddata.

DETAILED DESCRIPTION OF THE INVENTION

The present invention provides a biometric system for characterizingindividuals by the non-invasive sensing of the subject's pulse waveformfor the purposes of identification and identity tracking/confirmation.The major components include a computer processor, data storage, and apulse sensor adjacent to the subject's tissue that generates time-seriesdata based on the subject's pulse waveforms.

As an overview, the processor initially receives and analyzes the pulsewaveform data from the pulse sensor for a known subject to generatesubject characterization data identifying the known subject. Thereafter,in the identity authentication mode, the processor receives data fromthe pulse sensor for a test subject, who may or may not be the knownsubject. The processor analyzes this data in conjunction with the storedsubject characterization data to determine whether the test subject isthe same as the known subject. For the purposes of this application, itshould be understood that the phrase “test subject” refers to the personwhose identity is being tested or confirmed during the identityauthentication mode of the present system.

Thus, the present system operates in one of two mutually exclusivemodes—an enrollment mode and an identity authentication mode. Theenrollment mode acquires subject data under the supervision of a trainedtechnician, computes subject characteristics, calculates the probabilityof an impostor producing similar characteristics, and stores thesefindings in a client database for later use during the identityauthentication mode.

FIG. 1 is a general flowchart of the enrollment mode employed toinitially build subject characterization data for a known subject. Theoperator first verifies the identity of the subject (step 20), andmounts and tests the pulse sensor on the subject (step 21). Theprocessor acquires pulse waveform data from the pulse sensor for a briefperiod of time (step 22). The subject may be asked to undertake a rangeof activities to ensure the enrollment data are representative of thatwhich may be encountered over the subject's normal day-to-dayactivities. The processor analyzes the enrollment data and generatessubject characterization data for identifying the known subject (step23). This subject characterization data is stored for later use duringthe identity authentication mode of the present system (step 24), aswill be described below.

The identity authentication mode is used to authenticate the identity ofa test subject, who may or may not be the known subject from theenrollment mode. In this mode, the system acquires subject dataunsupervised in the field, compares it to subject and impostorcharacteristics, and decides whether to authenticate or challengeidentification. FIG. 2 is a flowchart of one possible embodiment of theidentity authentication mode. For each identity authentication test, theprocessor acquires pulse waveform data from the pulse sensor for thetest subject (step 25). The processor analyzes this test data using thesubject characterization data (step 26). Based on this analysis, in step27, the processor determines whether there is a sufficient degree ofsimilarity between the pulse waveform characteristics of the knownsubject (from the subject characterization data) and the test subject toconclude that these subjects are the same person (step 28). If so, theprocessor may update the subject characterization data 18 to include thecurrent test data (step 28A) and then loop back to step 25. Otherwise,if the processor determines that the current test subject is not thesame as the known subject, an alarm can be activated to signal thatdeception has been detected (step 29).

As will be discussed below, the two modes in the preferred embodiment ofthe present invention share a common “acquire trial” procedure thatacquires and pre-processes a short, contiguous time-series data of thedigitized measurement, called a “trial”.

FIG. 3 shows how the present system acquires a trial. The trial pulsewaveform typically consists of a small number (e.g., ten) of pulsecycles, which are similar but not identical to each other. Performanceis improved by synchronizing and summing pulse cycles to wash out noise.The goal of the procedure is to convert the multi-cycle waveform into asingle representative cycle, or exemplar. Block 300 is the start of theprocedure. Block 301 reads and discards pulse samples for a fixedduration (e.g., 1.5 seconds) while the waveform settles. After settling,block 302 reads and records samples for the remainder of the trial(e.g., 8.5 seconds).

Block 303 tests samples for “railing” (i.e., exceeding the limits of thedigitizer, an indicator of trial corruption) as shown for example inFIG. 7. Upon detecting a railed sample, block 303 calls block 304, theextrapolative peak restoration routine, which is discussed below. If norecorded samples are railed, or railed peaks are reparable, controlproceeds to block 305, which calculates the first and second derivativesof the pulse wave with respect to time, to eliminate baseline drift andgenerate triggers associated with the systolic excursion. Representingthe subject's pulse wave with its first derivative also obscures thebio-informational nature of the signal, thus enhancing privacy. Thederivatives may or may not be smoothed to reduce high frequency noise.Block 306 chooses the most negative excursion of the second derivativeas the “trigger candidate” (TC). Next, block 307 zeroes the TC and somesmall number (e.g., four) of immediate predecessor and successor data,to avoid selecting the same peak again. Then, block 308 compares thepresent TC to the first TC. If the present TC is greater than somethreshold fraction (e.g., ½) of the first TC, the procedure loopsthrough blocks 306-308, acquiring another TC. If not, TC acquisition isdeemed complete, and control proceeds to block 309. If there are many(e.g., eight) more TCs than can be accounted for according to thesettled sampling time and maximum pulse rate (e.g., 17 for 8.5 secondsat 120 beats per minute), the sample is judged too noisy, and block 309calls block 310, which rejects the trial and stops the procedure. Ifnot, the trial is accepted, but some of the TCs may be noise spikesasynchronous to the underlying pulse cycle.

The section labeled as blocks 311-318 is called the “trigger sieve”because it removes asynchronous false triggers, thus enhancingperformance. Block 311 calculates a square matrix of the delays Δbetween every pair of TCs. Next, the procedure loops through all integerpulse periods, in units of the sampling period, from the fastest to theslowest measurable pulse (e.g., 50-150 for 100 Hz sampling and 120-40beats per minute), to find the best fit to the preponderance of TCs.Block 312 increments the pulse period P. Block 313 computes the matrixof squared remainders [Δ mod P]², where the “mod” operation yields theinteger remainder with the smallest absolute value (e.g. 15 mod 8 equals−1, not 7). Block 314 sums the squared remainders for each TC relativeto the other TCs, and normalizes such that a “score” near (much smallerthan) unity indicates P is a poor (good) fit to the true pulse period.Block 315 averages the TC scores to evaluate P's goodness of fit. Block316 selects the P with the lowest score. Next, block 317 detects ifthere are TCs with optimal-P scores greater than a preset threshold(e.g., 0.25), or any clustered TCs, as false triggers not synchronizedwith the prevailing pulsatile rhythm. If there is at least one falsetrigger, block 318 eliminates the TC with the largest optimal-P score,and control returns to block 311. If there are no false triggers, block319 uses the true triggers to synchronize and sum the cycles, and block320 returns the summed cycle to the calling program.

The following is a discussion of the procedure for extrapolative peakrestoration (EPR) of clipped or railed pulse wave peaks in block 304.When prototyped, the prototype hardware appeared to have sufficientrange to capture pulse wave data without overdriving theanalog-to-digital convertor, or ADC. This is also known as clipping orrailing. Subsequently, some strong-signal subjects have demonstratedpulse waves whose peaks are beyond the limits of the ADC, as shown forexample in FIG. 7. One possible rule is to discard any trial that railsafter initial settling, potentially resulting in much spoiled data. Thealternative of reducing the sensitivity so that even the strongestpulses remain within bounds is unattractive because we also have severalweak-signal subjects whose pulse wave can scarcely be discerned as itis. Therefore, we desire a method for the extrapolative restoration ofthe clipped pulse wave peaks.

FIG. 7 shows an example of the data used to develop this model. Thisrepresents about the worst trial that should be restored (any trial withworse railing should be discarded). Data from the initial settling (thefirst 1.5 seconds) is not shown. There are three clipped waves, withwidths of 9, 22, and 19 consecutive ADC counts of zero respectively, forfifty total railed data. Clipping at the other rail 65535 is alsopossible, of course.

One embodiment of the present invention employs the following criteria:A railed trial should be restored if it has: (1) no clipped peak ofwidth greater than 25; (2) tails sufficiently long for restoration onboth sides of each clipped peak; and (3) has a total clipped width nogreater than 85 (10% of the post-settling data). Otherwise, it should bediscarded.

The method uses a Gaussian-weighted parabolic best fit. It marchesthrough the data set, separately restoring each clipped peak inchronological order. One may as well specify fit window as wide as theexemplar (e.g. 49 data), and symmetrical about the mid-point of therailed segment of length L:

${W(k)} = {\exp\lbrack {- \frac{k^{2}}{2\sigma^{2}}} \rbrack}$for |k|>½ (L−1) if L is odd, and for |k|>½ L if L is even, and W(k)=0for k otherwise; where (e.g. kε[−24,+24]) is defined relative to thecenter-most datum if L is odd, and to the earlier member of thecenter-most pair of data if L is even; and σ is a user-selected standarddeviation (e.g., eight). The best fit {tilde over (Z)}(k)=Ak²+Bk+C isfound by optimizing the fit coefficients using weighted least-squares.Because of symmetry, sums over odd powers of k vanish, yielding:

${\begin{bmatrix}{\Sigma\;{Wk}^{4}} & 0 & {\Sigma\;{Wk}^{2}} \\0 & {\Sigma\;{Wk}^{2}} & 0 \\{\Sigma\;{Wk}^{2}} & 0 & {\Sigma\; W}\end{bmatrix} \cdot \begin{bmatrix}A \\B \\C\end{bmatrix}} = \begin{bmatrix}{\Sigma\;{Wk}^{2}Z} \\{\Sigma\;{WkZ}} \\{\Sigma\;{WZ}}\end{bmatrix}$where Σ is a sum over k. Inverting this gives:

$B = {{\frac{\Sigma\;{WkZ}}{\Sigma\;{Wk}^{2}}\mspace{14mu}{{and}\mspace{14mu}\begin{bmatrix}A \\C\end{bmatrix}}} = {{\frac{1}{{\Sigma\;{Wk}^{4}\Sigma\; W} - ( {\Sigma\;{Wk}^{2}} )^{2}}\begin{bmatrix}{\Sigma\; W} & {{- \Sigma}\;{Wk}^{2}} \\{{- \Sigma}\;{Wk}^{2}} & {\Sigma\;{Wk}^{4}}\end{bmatrix}} \cdot \begin{bmatrix}{\Sigma\;{Wk}^{2}Z} \\{\Sigma\;{WZ}}\end{bmatrix}}}$Quantities independent of Z can be evaluated beforehand, and stored in alook-up table. Sums over products containing Z still require explicitcomputation. Once A, B, and C have been computed, the best fit {tildeover (Z)}(k)=Ak²+Bk+C can be calculated for kε[−24,+24], and thefollowing rule can be applied to restore the clipped peak: Z(k)←extremum({tilde over (Z)}(k),Z(k)), (i.e., choose the more extreme of the fitand measured values, independently for each k). This will tend to usethe fit values across the clipped segment as desired, but revert to themeasured values on the tails of the fit window, where the parabolic fitis poor.

FIG. 4 shows two embodiments of the procedure used to enroll a newclient. This procedure can be used both to establish the client'scharacteristics as a subject whose identity will be putative in thefield, and as a possible impostor for any other client. Block 500 is thestart of the procedure. Block 501 acquires a number of trial timeperiods (e.g., five or seven) by repeatedly calling the appropriate“Acquire Trial” procedure. Block 502 computes the exemplar (i.e., thearithmetic mean over the enrollment trials of any or all of the pulsewave shape vector and possibly other parameters, arranged into a vector)using some or all of the enrollment trials.

In one embodiment of the present invention, only the pulse waveform datafrom the most consistent trials are averaged to create the exemplaraccording to a predetermined rule in block 502. For example, the pulsewaveform data for the five most-typical trials out of seven total trialscan be averaged to create the exemplar. In other words, the two mostatypical trials are discarded. In another embodiment, the entire set ofenrollment trials for a subject can be rejected as being substandard andthe subject is then required to repeat the enrollment trial process. Forexample, the enrollment trials for a subject can be determined to besubstandard if the dynamically-weighted variance of the pulse waveformdata exceeds a predetermined threshold.

After block 502 has computed the exemplar, block 503 computes thestatistics (i.e., the covariance matrix) of the enrollment trial, aswell as the relative weights of the shape vector components. The lattermay incorporate either or both of two independent innovations: dynamicweighting, in which portions of the shape vector that are morerepeatable from trial to trial are accentuated relative to lessrepeatable portions; and feature weighting, in which portions of theshape vector that are more specific to the subject are accentuatedrelative to portions more typical of the population at large.

Block 504 transfers control to one of two blocks, depending on whetherthe “fixed authentication threshold” or the “Bayesian optimal decision”embodiment of the algorithm is selected. The chief distinction is thatthe Bayesian embodiment makes use of potential impostor data (i.e., fromother clients as potentials impostors for the subject), while the fixedthreshold does not. Block 505 finds the principal components of thecovariance matrix, and uses the dominant eigenvector (i.e., that withthe largest eigenvalue) to linearly combine the parameter vector into ascalar “composite parameter”, which is optimal in the sense that theenrollment data has the greatest correlation, and thus the least spread,along the dominant eigenvector. In general, this results in unequalweighting of the parameters in the decision to authenticate or challengeidentity. Next, block 506 computes the authentication thresholdcorresponding to the preset desired true authentication probability(e.g., ⅞). Then, block 507 enrolls the client, and block 508 stops theprocedure. On the other bifurcation, block 509 expands the ratio of thesubject probability density to the impostor probability density tosecond order in the deviation from the subject exemplar. Block 510includes the effects of the generally unequal penalties of falseauthentication and false challenge, and the a priori probability ofattempted deception, which varies among clients. Since the Bayesianoptimal decision embodiment uses the entire covariance matrix, it is notnecessary or advantageous to define a composite parameter; and sinceimpostor data is incorporated, the true and false authenticationprobabilities can be traded.

FIG. 5 shows how either algorithm embodiment decides whether toauthenticate or challenge the subject's identity based on a field trial.Block 600 is the start of the procedure. Block 601 acquires a fieldtrial, and block 602 subtracts the subject exemplar to yield the“deviation”, a vector with the same structure as a trial, and optionallyapplies dynamic or feature weighting to the deviations of the shapevectors. Block 603 transfers control to one of two blocks, depending onwhether the “fixed authentication threshold” or the “Bayesian optimaldecision” embodiment of the algorithm is selected. Block 604 computesthe optimal composite parameter for the deviation, and block 605compares it to the authentication threshold. If greater, block 606advises authorities to authenticate the subject's identity, and block607 stops the procedure. If lesser, block 608 advises authorities tochallenge the subject's identity. On the other bifurcation, block 609computes the ratio of the subject probability density to the impostorprobability density to second order in the deviation of the field trialfrom the subject exemplar, and block 610 compares it to one. If greaterthan one, block 606 advises authorities to authenticate the subject'sidentity. If not, block 608 advises authorities to challenge thesubject's identity.

As so far described, the algorithm uniformly weights each exemplar shapevector component, placing equal importance on the various features.However, this restriction is unnecessary, and may not be optimal. Someparts of some subjects' exemplars are more characteristic than otherparts, so it's reasonable to suppose weighting unusual features moreheavily could enhance the distinguishability of subjects.

FIG. 6 shows the pulse wave exemplar shape vectors of the ten subjectsof a recent study, along with the mean pulse wave shape. Generally, somesubjects are more atypical than others, and therefore are more easilyidentified in the field. Some subjects have features (e.g., subject26MJB near 0.27 seconds) that are quite distinctive. If these featuresare weighted more heavily than more typical regions (e.g., subject 26MJBnear 0.14 seconds), the subject is more readily recognized whensupplying a legitimate field trial, and less easily mimicked by animpostor. An example feature-weighting strategy is to weight each fieldtrial shape vector component proportionally to the square of thedeviation of the corresponding subject exemplar component from the meanexemplar component, thus placing greater weight on more unusualfeatures.

One technique for implementing dynamic weighting is to parse the shapevector into segments that are large enough to avoid excessivestatistical fluctuations, yet small enough to provide resolution of thevarying character across the vector (e.g., a 100-component pulsewaveform vector into 20 five-component segments), and assign a differentweight to each segment based on its fluctuations. An exampledynamic-weighting strategy is to weight each field trial shape vectorsegment proportionally to the reciprocal of the segment's variance(i.e., the sum over enrollment trials and segment components of thesquared deviation of the enrollment trial component from the exemplarcomponent), thus placing greater weight on more repeatable segments.

One technique for implementing feature weighting is to raise each shapevector component probability to a different power greater or less thanunity, according to how much the exemplar shape deviates from theaverage subject at that point. The feature weighting function can beexpressed as a vector of the same dimensionality as the shape itself,consisting of components whose average is unity (equal weighting isencompassed as the special case where all components are one). Thisapproach keeps the rest of the algorithm unaffected by whether featureweighting is selected or disabled. In general, the feature weightingvector is different for each client.

The above disclosure sets forth a number of embodiments of the presentinvention described in detail with respect to the accompanying drawings.Those skilled in this art will appreciate that various changes,modifications, other structural arrangements, and other embodimentscould be practiced under the teachings of the present invention withoutdeparting from the scope of this invention as set forth in the followingclaims.

We claim:
 1. A method for biometric identity confirmation of a subjecthaving a pulse, said method comprising: during an initial training mode,acquiring pulse waveform data from a known subject; generating andstoring subject characterization data for the known subject based atleast in part on an exemplar created by synchronous averaging of pulsewaveform data over multiple pulse cycles, wherein said synchronousaveraging of pulse waveform data includes: (a) identifying triggercandidates for a start point of each pulse cycle; (b) analyzing a timedelay between trigger candidates to discard false trigger candidates andidentify true trigger candidates; and (c) synchronously averaging thepulse waveform data for each pulse cycle using the true triggercandidates as start points for each pulse cycle; and during a subsequentidentity authentication mode, acquiring pulse waveform data from a testsubject, and analyzing the pulse waveform data with the subjectcharacterization data for the known subject to confirm whether theidentity of the test subject matches the known subject.
 2. The method ofclaim 1 wherein the trigger candidates are derived at least in part froma second derivative of the pulse waveform data with respect to time. 3.The method of claim 1 wherein the step of acquiring and analyzing pulsewaveform data for a test subject further comprises: acquiring pulsewaveform data from the test subject over multiple pulse cycles;identifying trigger candidates for the start point of each pulse cycle:analyzing the time delay between trigger candidates to discard falsetrigger candidates and identify true trigger candidates; andsynchronously averaging the pulse waveform data for each pulse cycleusing the true trigger candidates as the start points for each pulsecycle.
 4. The method of claim 1 wherein the step of generating subjectcharacterization data further comprises: computing an exemplar in theform of a parameter vector from the pulse waveform data for the knownsubject; computing a covariance matrix from the pulse wave data for theknown subject; computing an optimal composite parameter from thecovariance matrix and parameter vector that is characteristic of theknown subject; and computing an authentication threshold correspondingto a desired true authentication probability for the known subject. 5.The method of claim 1 wherein the step of analyzing the pulse waveformdata with the subject characterization data for the known subject toconfirm whether the identity of the test subject matches the knownsubject further comprises: computing a deviation of the pulse waveformdata for the test subject from the exemplar for the known subject:computing an optimal composite parameter from the deviation; andconfirming the identity of the test subject matches the known subject ifoptimal composite parameter is greater than the authentication thresholdfor the known subject.
 6. The method of claim 1 wherein the step ofgenerating subject characterization data further comprises: computing anexemplar in the form of a parameter vector from the pulse waveform datafor the known subject; computing a covariance matrix from the pulsewaveform data for the known subject; and computing a probabilitydistribution ratio of a weighted subject/impostor probability density bya Bayesian optimal decision analysis of the parameter vector, covariancematrix, and data from other subjects as potential impostors for theknown subject.
 7. The method of claim 1 wherein the step of analyzingthe pulse waveform data with the subject characterization data for theknown subject to confirm whether the identity of the test subjectmatches the known subject further comprises: computing a deviation ofthe pulse waveform data for the test subject from the exemplar for theknown subject; computing a weighted subject/impostor probability densityratio for the deviation; and confirming the identity of the test subjectmatches the known subject if the weighted subject/impostor probabilitydensity ratio is greater than one.
 8. The method of claim 1 wherein thestep of generating subject characterization data further comprisesweighting portions of the exemplar selected based on their repeatabilityobserved during the initial training mode.
 9. The method of claim 1wherein the step of generating subject characterization data furthercomprises weighting portions of the exemplar selected to distinguishcharacteristic features of the known subject observed during the initialtraining mode.
 10. The method of claim 1 wherein the initial trainingmode acquires a plurality of sets of pulse waveform data over aplurality of trials, and wherein the step of synchronous averagingincludes only the pulse waveform data from the most consistent trials.11. The method of claim 1 wherein the initial training mode acquires aplurality of sets of pulse waveform data over a plurality of trials, andwherein the trials are rejected if the variance of the pulse waveformdata exceeds a predetermined threshold.